
For a given class \(c\) , the negative mean squared Euclidean distance between \(Q\)  and \(\bar{Q}_c\)  over all feature map locations yields the scalar probability logit.
We also incorporate a learnable temperature factor \(\gamma \) , following
[1]}, [2]}, [3]}.
The final predicted probability is thus given by:
\(\langle Q,\bar{Q}_c \rangle &= \frac{1}{r}||Q-\bar{Q}_c||^2\\P(y_q=c|x_q) &= \frac{e^{(-\gamma \langle Q,\bar{Q}_c \rangle )} }{\sum _{c^{\prime }\in C} e^{(-\gamma \langle Q,\bar{Q}_{c^{\prime }} \rangle )} }\) 
